Given a continuous signal, I would like to convert into epoch.
Each epoch made from three marker (i.e., deviation onset, response onset, response offset), as illustrated below.
Extract epochs functions in
EEGLAB can automatically identified the 3 events and create an epoch, out of these markers as shown below.
As shown above, the 3 markers were preserved.
events = mne.events_from_annotations (raw) epochs = mne.Epochs(raw, events, preload=True) #or #epochs = mne.Epochs(raw, events, event_id=events,preload=True) epochs.plot()
While the epochs were successfully created, the 3 marker in the epochs is diminish as shown below.
I’m curious whether it is possible/ or how to achieved preserved this marker as in
.set file can be downloaded via the link: https://figshare.com/ndownloader/files/14249795
Also, may I know what is the proper setting to offset , so that we extract, 1 sec before the
deviation onset, and 1 sec after the
epochs = mne.Epochs(raw, events, preload=True,tmax=1,tmin=1)
Since along the pipeline I would like to do some analysis for
t-ResponseOffset, I wonder whether it is efficient to create epochs as I desire to achieve above? Or It is better to create separate epochs for each of
t-ResponseOffset. For example, epoch for time in between
DeviationOnset, and so on.
Thanks in advance for any insight/confirmation